The following description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
Digital image recognition is known and is utilized to recognize faces in images. Digital image recognition with respect to coins is also known. An example is described in U.S. Pat. No. 8,615,123.
Coin sorting and handling systems are known. Individuals can deposit a large number of coins (“bulk coins”) into a sorting and handling system, which system will sort the coins by size, stack and count the sorted coins, and wrap them in sleeves (or place them into a sleeve). Some such systems are known to provide individuals with a receipt, which receipt the individual can redeem for cash. The cash value on such receipts is based on the number of the coins and the coin denomination, less a handling fee, notwithstanding that certain of the coins may have a value greater than the denomination of the coin. Other than some systems which can determine metal types present in coins and provide a value based on the metal type, such systems cannot discriminate a high-value version of a coin (one worth more than the coin's denomination) from a conventional version of a coin (one worth the coin's denomination).
However, performing digital image recognition on bulk coins presents difficulties in terms of the speed at which digital image recognition can be performed relative to an imperative to process bulk coins in a short period of time and relative to the computational cost of performing digital image recognition. In particular, for large unsorted coin collections, discussed herein as “bulk coins,” the universe of coin types which need to be searched to perform digital image recognition is very large. For example, there are approximately 200,000 coin types in the Standard Catalog of World Coins (which is not the only or a completely exhaustive catalog of coin types). Template data required for reliable coin type sorting occupies approximately 1 MB of computer memory per coin type. Templates for all coins in the Standard Catalog of World Coins would, for example, require on the order of 200 GB (likely more) of template data to be stored in memory quickly accessible to the computational units (CPU or GPU or otherwise). If processing time for recognition of bulk coins is approximately 100 ms or better, bulk coins may be typed and recognized at a rate of approximately 10 coins per second, or 600 coins per minute. Just the computational resources capable of recognizing 10 different coin types at this pace costs on the order of $1200 to $5000 at the time this paper was prepared.
Machine recognition of a larger set of coin types in one pass through a coin handling system using existing approaches, even when leveraging the great parallel computing power of GPUs, would require a prohibitively expensive computer system, consumption of a large amount of electricity, and a large physical plant just for the computer system, disregarding the machinery required to handle the coins. Moore's Law appears to continue to operate, but the cost of a coin recognition system which can rapidly recognize a large number of different coin types in a single pass or even several such passes will remain prohibitive for most intended users for years to come.
To partially address this issue, existing bulk coin recognition systems process coins by multiple applications of the single-pass operation, one pass for each (very small) subset of coin types which the system can recognize. However, multiple such passes are not desirable because each pass requires that a person physically relocate the coins from the outlet back to the inlet, because each pass causes wear on the coins, because the coin handling machinery needs to be maintained, because the multiple passes multiply the total processing time and decrease the productivity of the system, because it is ignorant of metrics specific to a specific lot of bulk coins, which metrics can be used to greatly enhance true positive identification and classification rates, and because the residual coins in each successive pass become more and more obscure, resulting in passes which return no recognition results. These issues also reduce the extent to which more complex recognition processes can be executed to identify the multitude of coin attributes beyond type which also contribute to the value of a coin, such as year, mint-mark, wear, conditional die varieties, or post-minting marks.
In addition, some known high-volume coin processing systems, such as the one described in U.S. Pat. No. 8,615,123, utilize “dark field” images, wherein the coins are imaged with a light source which is almost parallel to the coin surface. This approach highlights elevation changes in the coin surface, leaving the rest of the image field dark. However, the resulting images are not useful for humans, such as to determine eye appeal for a coin based on the image.
Needed is a reasonably priced, sized, and electrically powered coin recognition system which can recognize a large number of different coin attributes and types, which can determine other, complex coin attributes with only one or two passes, which uses “bright field” images which can also be used by people, and which requires less maintenance and less movement of bins of coins by people.